Coupling of Replica Exchange Simulations to a Non-Boltzmann Structure Reservoir

Roitberg, Adrián E.; Okur, and Asim; Simmerling, Carlos

doi:10.1021/jp068335b

Cited by 84 publications

(100 citation statements)

References 32 publications

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“…Generalized ensemble (GE) algorithms, such as the replica exchange method (REM), or parallel tempering (1,2) and simulated tempering (ST) (3,4), are popular approaches for studying biomolecular folding (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). They attempt to overcome the sampling problem by inducing a random walk in temperature space while maintaining canonical sampling at each temperature.…”

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confidence: 99%

Rapid equilibrium sampling initiated from nonequilibrium data

Huang

Bowman

Bacallado

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

157

179

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Simulating the conformational dynamics of biomolecules is extremely difficult due to the rugged nature of their free energy landscapes and multiple long-lived, or metastable, states. Generalized ensemble (GE) algorithms, which have become popular in recent years, attempt to facilitate crossing between states at low temperatures by inducing a random walk in temperature space. Enthalpic barriers may be crossed more easily at high temperatures; however, entropic barriers will become more significant. This poses a problem because the dominant barriers to conformational change are entropic for many biological systems, such as the short RNA hairpin studied here. We present a new efficient algorithm for conformational sampling, called the adaptive seeding method (ASM), which uses nonequilibrium GE simulations to identify the metastable states, and seeds short simulations at constant temperature from each of them to quantitatively determine their equilibrium populations. Thus, the ASM takes advantage of the broad sampling possible with GE algorithms but generally crosses entropic barriers more efficiently during the seeding simulations at low temperature. We show that only local equilibrium is necessary for ASM, so very short seeding simulations may be used. Moreover, the ASM may be used to recover equilibrium properties from existing datasets that failed to converge, and is well suited to running on modern computer clusters.generalized ensemble methods ͉ Markov state models ͉ molecular dynamics simulations ͉ RNA hairpin folding T he functions of biological macromolecules are in large part determined by their structure and dynamics. As such, many experimental techniques have been developed and applied to probe these properties, each of which has its strengths and weaknesses. Computational methods such as molecular dynamics (MD) and Monte Carlo (MC) simulations have the potential to complement such experiments by modeling the evolution of entire systems with atomic resolution. However, it is extremely difficult to obtain equilibrium sampling of even moderately sized systems in atomic simulations because of the rugged nature of the free energy landscapes that must be explored. Without adequate sampling, it is impossible to validate the parameters, or force fields, that determine the interactions between atoms or to address phenomena that occur on biologically relevant timescales.Many methods have been developed in an attempt to address the sampling problem. Generalized ensemble (GE) algorithms, such as the replica exchange method (REM), or parallel tempering (1, 2) and simulated tempering (ST) (3, 4), are popular approaches for studying biomolecular folding (5-15). They attempt to overcome the sampling problem by inducing a random walk in temperature space while maintaining canonical sampling at each temperature. At high temperatures, energetic barriers may be crossed easily; at low temperatures, the system is generally constrained to local minima. However, recent studies have shown that GE simulations do not yield conver...

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mentioning

confidence: 99%

Rapid equilibrium sampling initiated from nonequilibrium data

Huang

Bowman

Bacallado

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

157

179

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“…This issue may be alleviated by Generalized Ensemble (GE) algorithms [76][77][78][79], which can enhance conformational sampling by inducing a random walk in Temperature or Hamiltonian space. In recent years, GE algorithms especially Replica Exchange Method (REM) [78,79] have been widely applied in protein folding studies [14,[80][81][82][83][84][85][86][87][88][89][90][91]. Huang et al [92] have used non-equilibrium GE simulations to explore the phase space, and then seed short simulations at constant temperature from GE conformations to construct MSMs to obtain both equilibrium thermodynamics and kinetics.…”

Section: P(n T) = [T( T)]mentioning

confidence: 99%

Bridging the Gap Between Optical Spectroscopic Experiments and Computer Simulations for Fast Protein Folding Dynamics

Cui¹,

Silva²,

Song³

et al. 2012

CPC

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Fast folding techniques use optical spectroscopic tools to monitor protein folding or unfolding dynamics after a fast triggering such as the laser induced temperature jump. These techniques have greatly improved time resolution of experiments and provide new opportunities for comparison between theory and simulations. However, the direct comparison is still difficult due to two main challenges: a gap between folding relevant timescales (microseconds or above) and length of molecular dynamics simulations (typically tens to hundreds of nanoseconds), and difficulty in directly calculating spectroscopic observables from simulation configurations. This review is focused on recent advances in addressing these two challenges. We describe new methodology that allows simulating folding timescales with an emphasis on Markov State Models. We also review progress on modeling infrared, circular dichroism, and fluorescence spectroscopic signals from protein conformations. At last, we discuss a few studies that directly simulate time-resolved spectroscopy of temperature jump induced unfolding dynamics for a few small proteins. These studies not only provide direct validation of theoretical models, but also greatly improve our understanding of protein folding mechanisms by connecting ensemble averaged spectroscopic observables with atomistic protein conformations.

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“…The computational simulations, on the other hand, might mimic and model this kind of interactions and avoid some complicated side effects. Molecular dynamics (MD) is one of these computational techniques, which is widely used in the studies of biomolecules [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55], and nanoscale systems [56][57][58][59][60]. It is found effective in providing insights to the interactions between proteins and CNTs.…”

Section: Introductionmentioning

confidence: 99%